The present invention relates generally to magnetic resonance (MR) imaging and, more particularly, to a method of MR imaging using parallel imaging reconstruction buttressed by a non-static regularization image to improve spatiotemporal resolution of dynamic images.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B0) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, or “longitudinal magnetization”, MZ, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited spins after the excitation signal B1 is terminated and this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct an image.
Dynamic imaging, which is directed to the acquisition of images from objects in motion, is increasingly being used for cardiac imaging, contrast-enhanced dynamic studies, and functional imaging. With conventional dynamic imaging protocols, frames of data are acquired and individually reconstructed to form a series of images which collectively show motion within the region-of-interest from which the data was collected. Ideally, it is desirable to acquire each temporal frame with fully encoded samples to achieve high spatial resolution; however, acquiring such fully sampled frames would require excessive acquisition times thereby negatively affecting the temporal resolution of the data acquisition. Therefore, to improve the spatial resolution without significantly affecting temporal resolution of dynamic images, a number of dynamic acquisition techniques have been developed.
Generally, these proposed dynamic imaging techniques undersample k-space to reduce data collection time for each temporal frame. The techniques then use inherent spatial (k-space or image space) and/or temporal correlations among the samples to recover unsampled data. These techniques are generally classified into one of two categories: parametric and nonparametric. Those techniques that utilize a parametric model include Dynamic Imaging by Model Estimation (DIME), Reduced-encoding Imaging by Generalized-series Reconstruction (RIGR), and Broad-use Linear Acquisition Speed-up Technique (BLAST). Nonparametric techniques include partial Fourier imaging, keyhole imaging, parallel imaging, and UNaliasing by Fourier-encoding the Overlaps using the temporaL Dimension (UNFOLD). Parallel imaging, which is independent of other techniques, is increasingly being used in combination with other imaging techniques to provide further improvement in spatiotemporal resolution. These combinations include TSENSE, UNFOLD-SENSE, TGRAPPA, and ktBLAST/ktSENSE.
While these proposed techniques have been effective in improving spatiotemporal resolution of dynamic images, the techniques do have some drawbacks. For example, RIGR, which provides the advantage of capturing dynamic information with efficient representation, assumes that changes between acquired reference frames and dynamic references have low spatial resolution and can be acquired by central dynamic encodings. As a result, with the RIGR technique, new dynamic features or sharp changes between frames must have low spatial resolution and, thus, potentially mask diagnostically valuable information.
Similarly, the parallel imaging technique ktBLAST/ktSENSE also suffers from some drawbacks. (Because ktBLAST is the single channel version of ktSENSE, reference to ktSENSE shall include ktBLAST.) The ktSENSE technique utilizes a static regularization image. Specifically, ktSENSE uses a temporal average (DC) term which, as a result, can decrease the temporal resolution of the ktSENSE images significantly. Moreover, because ktSENSE uses training data that is acquired with a pre-scan, it is possible for misregistration to occur between training data and imaging data during synchronization of the training and imaging data. This can introduce additional errors into the ktSENSE images. Notwithstanding these drawbacks, ktSENSE is effective in acquiring data with high temporal resolution through the undersampling of phase encodings with a given reduction factor for each temporal frame. KtSENSE also is efficient in recapturing the true signal from aliased signals.
It would therefore be desirable to have a data acquisition and reconstruction process that carries out ktSENSE or other similar parallel imaging technique with a non-static regularization image, such as that generated by RIGR, to provide dynamic images with improved spatiotemporal resolution.